IFTSIP: interval fuzzy two-stage stochastic mixed-integer linear programming: a case study for environmental management and planning

Abstract

In this study, an interval fuzzy two-stage stochastic mixed-integer linear programming (IFTSIP) method is developed for planning waste-management systems under uncertainty. As a new extension of mathematical programming methods, the developed IFTSIP approach has advantages in uncertainty reflection, policy analysis and capacity expansion. Methods of two-stage stochastic programming and interval fuzzy linear programming are introduced to a mixed-integer linear programming framework to effectively tackle uncertainties described in terms of probability density functions, fuzzy membership functions and discrete intervals. The IFTSIP method can incorporate pre-defined waste management policies directly into its optimization process and can be used for analyzing various policy scenarios associated with different levels of economic penalties when the promised policy targets are violated. Moreover, it can obtain optimal decisions of capacity-expansion schemes for waste management within a multi-stage context. The developed method is applied to a case study of waste allocation and facility-capacity expansion within a municipal solid-waste management system. The results indicate that reasonable solutions for binary and continuous variables have been obtained. They will allow in-depth analyses of tradeoffs between environmental and economic objectives as well as those between system cost and decision-maker's satisfaction degree.